Aspect-level sentiment analysis method and device based on graph convolutional neural network

Abstract

The embodiment of the invention provides an aspect-level sentiment analysis method and device based on a graph convolutional neural network. The method comprises the steps of acquiring sentences to besubjected to aspect sentiment analysis and aspect words in the sentences to be subjected to aspect sentiment analysis; preprocessing the sentences and the aspect words to be subjected to aspect sentiment analysis to obtain input vector sequences and syntactic weighted graphs corresponding to the sentences to be subjected to aspect sentiment analysis; and inputting the input vector sequence and the syntax weighted graph into a pre-trained double-graph convolutional neural network to obtain an emotion analysis result corresponding to the aspect word. According to the embodiment of the invention, the dual-graph convolutional neural network not only pays attention to syntactic features of the sentences, but also pays attention to semantic features of the sentences and extracts semantic related features corresponding to the sentences, so that the defect that syntactic feature extraction of sentences insensitive to syntactics is inaccurate is overcome, and the accuracy of emotion analysis results is improved.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, in particular to an aspect-level sentiment analysis method and device based on a graph convolutional neural network. Background technique [0002] ABSA (Aspect-based Sentiment Analysis, aspect-based sentiment analysis) is an entity-level fine-grained sentiment analysis task, which aims to judge the emotional polarity of a given aspect word in a sentence. Aspect-level sentiment analysis can more accurately identify the user's emotional attitude towards a specific aspect, rather than directly judging the emotional polarity at the sentence-level granularity. [0003] The existing aspect-level sentiment analysis uses the GCN (Graph Convolution Network, graph convolution network) based on the syntactic dependency tree. Specifically, the sentence to be subjected to aspect sentiment analysis is used as input information, and the pre-trained Glove word embedding is used to inp...

AI Technical Summary

Problems solved by technology

[0004] However, the inventors found that in the existing process of implementing aspect-level sentiment analysis using the GCN based on the syntactic dependency tree, the parsing results are inaccurate due to the syntactic analysis of the sentence by the syntactic analyzer, and when the sentence to be sentimentally analyzed When it is not sensitive to syntactic dependence, the undirected graph converted from the syntactic dependency tree parsed by the dependency syntactic analyzer is used as an input of GCN, which makes the sentiment analysis result obtained by GCN inaccurate

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